1. Field of the Invention
The present invention relates to imaging systems. More specifically, the present invention relates to automatic target recognition and verification systems and methods.
2. Description of the Related Art
Imaging systems are currently used in a variety of military, industrial, commercial and consumer applications. Various technologies are available for providing image data including cameras operating in the visible portion of the spectrum, infrared sensors, and radar based systems.
For certain, e.g., military, applications, a large number of sensing systems are employed operating in various portions of the electromagnetic spectrum. These systems interrogate numerous sensors and generate substantial image data. Previously, the data was interpreted manually by a human operator or analyst. However, this approach is becoming impractical as the number of sensors and sensing systems increase and the output rate thereof climbs. Accordingly, for such demanding applications, an automatic data interpretation capability is needed.
Automatic target recognition (ATR) is one type of data interpretation system. Currently, ATR systems and methods fall into two broad classifications: statistical and model-based. Generally, statistical systems are trained to recognize targets based on empirical target data. While this approach can perform adequately under highly constrained conditions, it tends to be less than sufficiently robust with respect to signature variability. This leads to a requirement for a substantial amount of data and is therefore problematic for certain current and planned applications.
Model-based systems generally use synthetic predictions of target signatures to make recognition decisions. In this approach the system is provided with a model of a target and it predicts what the target might look like under various conditions and in various environments. Although this approach can display consistent performance across varying scene conditions, it is subject to limitations in current signature prediction technology.
In general, both approaches are limited by the fact that optical imagery is highly variable and inconsistent. Images derived from the same scene under the same conditions by the same sensor will vary considerably. This leads to challenges in automatic recognition that are exacerbated in military applications by targets that endeavor to avoid recognition.
Hence, a need remains in the art for an automatic target recognition system that is robust to signature variability and is not subject to current limitations in signature prediction technology.